Overview

Dataset statistics

Number of variables13
Number of observations2988181
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory296.4 MiB
Average record size in memory104.0 B

Variable types

Numeric11
Categorical2

Alerts

session_start has a high cardinality: 646874 distinct values High cardinality
click_timestamp has a high cardinality: 2983198 distinct values High cardinality
Unnamed: 0 is highly correlated with session_idHigh correlation
session_id is highly correlated with Unnamed: 0High correlation
Unnamed: 0 is highly correlated with session_idHigh correlation
session_id is highly correlated with Unnamed: 0High correlation
click_deviceGroup is highly correlated with click_osHigh correlation
click_os is highly correlated with click_deviceGroupHigh correlation
Unnamed: 0 is highly correlated with session_idHigh correlation
session_id is highly correlated with Unnamed: 0High correlation
Unnamed: 0 is highly correlated with user_id and 1 other fieldsHigh correlation
user_id is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
session_id is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
click_deviceGroup is highly correlated with click_osHigh correlation
click_os is highly correlated with click_deviceGroupHigh correlation
click_country is highly correlated with click_regionHigh correlation
click_region is highly correlated with click_countryHigh correlation
Unnamed: 0 is uniformly distributed Uniform
click_timestamp is uniformly distributed Uniform
Unnamed: 0 has unique values Unique

Reproduction

Analysis started2022-08-28 12:12:50.057321
Analysis finished2022-08-28 12:17:09.333827
Duration4 minutes and 19.28 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct2988181
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1494090
Minimum0
Maximum2988180
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-08-28T14:17:09.577864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile149409
Q1747045
median1494090
Q32241135
95-th percentile2838771
Maximum2988180
Range2988180
Interquartile range (IQR)1494090

Descriptive statistics

Standard deviation862613.6967
Coefficient of variation (CV)0.577350559
Kurtosis-1.2
Mean1494090
Median Absolute Deviation (MAD)747045
Skewness-1.014664281 × 10-15
Sum4.46461135 × 1012
Variance7.441023897 × 1011
MonotonicityStrictly increasing
2022-08-28T14:17:09.835835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
19921141
 
< 0.1%
19921161
 
< 0.1%
19921171
 
< 0.1%
19921181
 
< 0.1%
19921191
 
< 0.1%
19921201
 
< 0.1%
19921211
 
< 0.1%
19921221
 
< 0.1%
19921231
 
< 0.1%
Other values (2988171)2988171
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
29881801
< 0.1%
29881791
< 0.1%
29881781
< 0.1%
29881771
< 0.1%
29881761
< 0.1%
29881751
< 0.1%
29881741
< 0.1%
29881731
< 0.1%
29881721
< 0.1%
29881711
< 0.1%

user_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct322897
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107947.8258
Minimum0
Maximum322896
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-08-28T14:17:10.092829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6370
Q140341
median86229
Q3163261
95-th percentile274162
Maximum322896
Range322896
Interquartile range (IQR)122920

Descriptive statistics

Standard deviation83648.36147
Coefficient of variation (CV)0.7748962136
Kurtosis-0.4686650537
Mean107947.8258
Median Absolute Deviation (MAD)57248
Skewness0.7231189115
Sum3.22567642 × 1011
Variance6997048377
MonotonicityNot monotonic
2022-08-28T14:17:10.559860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58901232
 
< 0.1%
73574939
 
< 0.1%
15867900
 
< 0.1%
80350783
 
< 0.1%
15275746
 
< 0.1%
2151722
 
< 0.1%
4568529
 
< 0.1%
12897513
 
< 0.1%
11521502
 
< 0.1%
34541501
 
< 0.1%
Other values (322887)2980814
99.8%
ValueCountFrequency (%)
08
 
< 0.1%
112
 
< 0.1%
24
 
< 0.1%
317
 
< 0.1%
47
 
< 0.1%
587
< 0.1%
635
< 0.1%
722
 
< 0.1%
856
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
3228962
< 0.1%
3228952
< 0.1%
3228942
< 0.1%
3228932
< 0.1%
3228922
< 0.1%
3228912
< 0.1%
3228902
< 0.1%
3228892
< 0.1%
3228882
< 0.1%
3228873
< 0.1%

session_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1048594
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.507472228 × 1015
Minimum1.506825423 × 1015
Maximum1.508211379 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-08-28T14:17:10.818829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.506825423 × 1015
5-th percentile1.506941766 × 1015
Q11.507124152 × 1015
median1.50749334 × 1015
Q31.507749414 × 1015
95-th percentile1.508153221 × 1015
Maximum1.508211379 × 1015
Range1.385955918 × 1012
Interquartile range (IQR)6.252618534 × 1011

Descriptive statistics

Standard deviation3.855244602 × 1011
Coefficient of variation (CV)0.0002557423301
Kurtosis-1.111389169
Mean1.507472228 × 1015
Median Absolute Deviation (MAD)3.329949664 × 1011
Skewness0.1807598817
Sum3.594316782 × 1018
Variance1.486291094 × 1023
MonotonicityNot monotonic
2022-08-28T14:17:11.082829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.507563658 × 1015124
 
< 0.1%
1.507896573 × 1015107
 
< 0.1%
1.507133568 × 1015106
 
< 0.1%
1.507309773 × 101598
 
< 0.1%
1.508112331 × 101594
 
< 0.1%
1.507647366 × 101592
 
< 0.1%
1.507475404 × 101586
 
< 0.1%
1.506959499 × 101582
 
< 0.1%
1.508154737 × 101579
 
< 0.1%
1.506999909 × 101575
 
< 0.1%
Other values (1048584)2987238
> 99.9%
ValueCountFrequency (%)
1.506825423 × 10152
< 0.1%
1.506825426 × 10152
< 0.1%
1.506825435 × 10152
< 0.1%
1.506825443 × 10152
< 0.1%
1.506825528 × 10152
< 0.1%
1.506825541 × 10153
< 0.1%
1.506825553 × 10152
< 0.1%
1.506825568 × 10152
< 0.1%
1.506825573 × 10153
< 0.1%
1.506825599 × 10152
< 0.1%
ValueCountFrequency (%)
1.508211379 × 10152
 
< 0.1%
1.508211376 × 10152
 
< 0.1%
1.508211372 × 10152
 
< 0.1%
1.508211369 × 10157
< 0.1%
1.508211367 × 10152
 
< 0.1%
1.508211353 × 10154
< 0.1%
1.508211348 × 10152
 
< 0.1%
1.508211326 × 10152
 
< 0.1%
1.508211326 × 10154
< 0.1%
1.508211324 × 10152
 
< 0.1%

session_start
Categorical

HIGH CARDINALITY

Distinct646874
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size22.8 MiB
2017-10-09 15:40:57
 
127
2017-10-13 12:09:33
 
112
2017-10-04 16:12:47
 
108
2017-10-06 17:09:33
 
98
2017-10-10 14:56:06
 
97
Other values (646869)
2987639 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters56775439
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-10-01 02:37:03
2nd row2017-10-01 02:37:03
3rd row2017-10-01 02:37:06
4th row2017-10-01 02:37:06
5th row2017-10-01 02:37:15

Common Values

ValueCountFrequency (%)
2017-10-09 15:40:57127
 
< 0.1%
2017-10-13 12:09:33112
 
< 0.1%
2017-10-04 16:12:47108
 
< 0.1%
2017-10-06 17:09:3398
 
< 0.1%
2017-10-10 14:56:0697
 
< 0.1%
2017-10-16 00:05:3196
 
< 0.1%
2017-10-02 15:51:3987
 
< 0.1%
2017-10-10 16:05:4387
 
< 0.1%
2017-10-08 15:10:0386
 
< 0.1%
2017-10-16 11:52:1785
 
< 0.1%
Other values (646864)2987198
> 99.9%

Length

2022-08-28T14:17:11.330830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-10-02305709
 
5.1%
2017-10-10281384
 
4.7%
2017-10-03259709
 
4.3%
2017-10-09249856
 
4.2%
2017-10-11238521
 
4.0%
2017-10-04215267
 
3.6%
2017-10-06207537
 
3.5%
2017-10-16190891
 
3.2%
2017-10-05190074
 
3.2%
2017-10-13180599
 
3.0%
Other values (83818)3656815
61.2%

Most occurring characters

ValueCountFrequency (%)
111434946
20.1%
010649945
18.8%
25982278
10.5%
-5976362
10.5%
:5976362
10.5%
73949992
 
7.0%
2988181
 
5.3%
32391815
 
4.2%
42104829
 
3.7%
52074593
 
3.7%
Other values (3)3246136
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41834534
73.7%
Dash Punctuation5976362
 
10.5%
Other Punctuation5976362
 
10.5%
Space Separator2988181
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
111434946
27.3%
010649945
25.5%
25982278
14.3%
73949992
 
9.4%
32391815
 
5.7%
42104829
 
5.0%
52074593
 
5.0%
61210846
 
2.9%
91111614
 
2.7%
8923676
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-5976362
100.0%
Other Punctuation
ValueCountFrequency (%)
:5976362
100.0%
Space Separator
ValueCountFrequency (%)
2988181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common56775439
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
111434946
20.1%
010649945
18.8%
25982278
10.5%
-5976362
10.5%
:5976362
10.5%
73949992
 
7.0%
2988181
 
5.3%
32391815
 
4.2%
42104829
 
3.7%
52074593
 
3.7%
Other values (3)3246136
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII56775439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
111434946
20.1%
010649945
18.8%
25982278
10.5%
-5976362
10.5%
:5976362
10.5%
73949992
 
7.0%
2988181
 
5.3%
32391815
 
4.2%
42104829
 
3.7%
52074593
 
3.7%
Other values (3)3246136
 
5.7%

session_size
Real number (ℝ≥0)

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.901885127
Minimum2
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-08-28T14:17:11.585829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median3
Q34
95-th percentile9
Maximum124
Range122
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.929941495
Coefficient of variation (CV)1.007190465
Kurtosis158.4608899
Mean3.901885127
Median Absolute Deviation (MAD)1
Skewness9.090074854
Sum11659539
Variance15.44444016
MonotonicityNot monotonic
2022-08-28T14:17:11.838829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21260372
42.2%
3670185
22.4%
4374240
 
12.5%
5220105
 
7.4%
6135762
 
4.5%
788354
 
3.0%
858544
 
2.0%
940878
 
1.4%
1029530
 
1.0%
1121714
 
0.7%
Other values (62)88497
 
3.0%
ValueCountFrequency (%)
21260372
42.2%
3670185
22.4%
4374240
 
12.5%
5220105
 
7.4%
6135762
 
4.5%
788354
 
3.0%
858544
 
2.0%
940878
 
1.4%
1029530
 
1.0%
1121714
 
0.7%
ValueCountFrequency (%)
124124
< 0.1%
107107
< 0.1%
106106
< 0.1%
9898
< 0.1%
9494
< 0.1%
9292
< 0.1%
8686
< 0.1%
8282
< 0.1%
7979
< 0.1%
7575
< 0.1%

click_article_id
Real number (ℝ≥0)

Distinct46033
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194922.6487
Minimum3
Maximum364046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-08-28T14:17:12.087827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile42223
Q1124228
median202381
Q3277067
95-th percentile336254
Maximum364046
Range364043
Interquartile range (IQR)152839

Descriptive statistics

Standard deviation90768.42147
Coefficient of variation (CV)0.4656638009
Kurtosis-0.943045904
Mean194922.6487
Median Absolute Deviation (MAD)77632
Skewness-0.1234365434
Sum5.824641553 × 1011
Variance8238906336
MonotonicityNot monotonic
2022-08-28T14:17:12.339827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16097437213
 
1.2%
27214328943
 
1.0%
33622123851
 
0.8%
23469823499
 
0.8%
12390923122
 
0.8%
33622321855
 
0.7%
9621021577
 
0.7%
16265521062
 
0.7%
18317620303
 
0.7%
16862319526
 
0.7%
Other values (46023)2747230
91.9%
ValueCountFrequency (%)
31
< 0.1%
271
< 0.1%
691
< 0.1%
812
< 0.1%
841
< 0.1%
942
< 0.1%
1152
< 0.1%
1251
< 0.1%
1371
< 0.1%
1391
< 0.1%
ValueCountFrequency (%)
3640462
 
< 0.1%
3640438
 
< 0.1%
3640281
 
< 0.1%
3640221
 
< 0.1%
36401722
< 0.1%
3640151
 
< 0.1%
3640141
 
< 0.1%
3640131
 
< 0.1%
3640121
 
< 0.1%
3640014
 
< 0.1%

click_timestamp
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2983198
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size22.8 MiB
2017-10-02 16:16:49.961
 
3
2017-10-06 20:07:23.928
 
3
2017-10-13 14:39:48.690
 
3
2017-10-16 14:42:54.899
 
3
2017-10-14 12:28:25.656
 
3
Other values (2983193)
2988166 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters68728163
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2978224 ?
Unique (%)99.7%

Sample

1st row2017-10-01 03:00:28.020
2nd row2017-10-01 03:00:58.020
3rd row2017-10-01 03:03:37.951
4th row2017-10-01 03:04:07.951
5th row2017-10-01 03:04:50.575

Common Values

ValueCountFrequency (%)
2017-10-02 16:16:49.9613
 
< 0.1%
2017-10-06 20:07:23.9283
 
< 0.1%
2017-10-13 14:39:48.6903
 
< 0.1%
2017-10-16 14:42:54.8993
 
< 0.1%
2017-10-14 12:28:25.6563
 
< 0.1%
2017-10-03 17:40:48.6433
 
< 0.1%
2017-10-02 20:16:02.2563
 
< 0.1%
2017-10-09 13:01:34.0453
 
< 0.1%
2017-10-02 14:54:37.2613
 
< 0.1%
2017-10-15 21:06:30.9582
 
< 0.1%
Other values (2983188)2988152
> 99.9%

Length

2022-08-28T14:17:12.648829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-10-02303177
 
5.1%
2017-10-10282391
 
4.7%
2017-10-03261159
 
4.4%
2017-10-09248208
 
4.2%
2017-10-11238969
 
4.0%
2017-10-04215415
 
3.6%
2017-10-06207646
 
3.5%
2017-10-05190003
 
3.2%
2017-10-16189779
 
3.2%
2017-10-13180723
 
3.0%
Other values (2923727)3658892
61.2%

Most occurring characters

ValueCountFrequency (%)
112265879
17.8%
011533489
16.8%
26914494
10.1%
-5976362
8.7%
:5976362
8.7%
74849332
 
7.1%
33299611
 
4.8%
43017099
 
4.4%
2988181
 
4.3%
.2988181
 
4.3%
Other values (4)8919173
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50799077
73.9%
Other Punctuation8964543
 
13.0%
Dash Punctuation5976362
 
8.7%
Space Separator2988181
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
112265879
24.1%
011533489
22.7%
26914494
13.6%
74849332
 
9.5%
33299611
 
6.5%
43017099
 
5.9%
52978574
 
5.9%
62106971
 
4.1%
92008543
 
4.0%
81825085
 
3.6%
Other Punctuation
ValueCountFrequency (%)
:5976362
66.7%
.2988181
33.3%
Dash Punctuation
ValueCountFrequency (%)
-5976362
100.0%
Space Separator
ValueCountFrequency (%)
2988181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common68728163
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
112265879
17.8%
011533489
16.8%
26914494
10.1%
-5976362
8.7%
:5976362
8.7%
74849332
 
7.1%
33299611
 
4.8%
43017099
 
4.4%
2988181
 
4.3%
.2988181
 
4.3%
Other values (4)8919173
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII68728163
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112265879
17.8%
011533489
16.8%
26914494
10.1%
-5976362
8.7%
:5976362
8.7%
74849332
 
7.1%
33299611
 
4.8%
43017099
 
4.4%
2988181
 
4.3%
.2988181
 
4.3%
Other values (4)8919173
13.0%

click_environment
Real number (ℝ≥0)

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.942652068
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-08-28T14:17:12.864862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median4
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.339680408
Coefficient of variation (CV)0.0861553092
Kurtosis33.01323632
Mean3.942652068
Median Absolute Deviation (MAD)0
Skewness-5.848728196
Sum11781358
Variance0.1153827796
MonotonicityNot monotonic
2022-08-28T14:17:13.073836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
42904478
97.2%
279743
 
2.7%
13960
 
0.1%
ValueCountFrequency (%)
13960
 
0.1%
279743
 
2.7%
42904478
97.2%
ValueCountFrequency (%)
42904478
97.2%
279743
 
2.7%
13960
 
0.1%

click_deviceGroup
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.819305792
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-08-28T14:17:13.283830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile3
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.042213782
Coefficient of variation (CV)0.5728634442
Kurtosis-1.427040365
Mean1.819305792
Median Absolute Deviation (MAD)0
Skewness0.5763858618
Sum5436415
Variance1.086209567
MonotonicityNot monotonic
2022-08-28T14:17:13.500858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
11823162
61.0%
31047086
35.0%
4117640
 
3.9%
5283
 
< 0.1%
210
 
< 0.1%
ValueCountFrequency (%)
11823162
61.0%
210
 
< 0.1%
31047086
35.0%
4117640
 
3.9%
5283
 
< 0.1%
ValueCountFrequency (%)
5283
 
< 0.1%
4117640
 
3.9%
31047086
35.0%
210
 
< 0.1%
11823162
61.0%

click_os
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.27760333
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-08-28T14:17:13.731829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median17
Q317
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)15

Descriptive statistics

Standard deviation6.881718417
Coefficient of variation (CV)0.5182952258
Kurtosis-0.9317514661
Mean13.27760333
Median Absolute Deviation (MAD)0
Skewness-0.9541171292
Sum39675882
Variance47.35804837
MonotonicityNot monotonic
2022-08-28T14:17:13.946829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
171738138
58.2%
2788699
26.4%
20369586
 
12.4%
1260096
 
2.0%
1323711
 
0.8%
196384
 
0.2%
51513
 
0.1%
354
 
< 0.1%
ValueCountFrequency (%)
2788699
26.4%
354
 
< 0.1%
51513
 
0.1%
1260096
 
2.0%
1323711
 
0.8%
171738138
58.2%
196384
 
0.2%
20369586
 
12.4%
ValueCountFrequency (%)
20369586
 
12.4%
196384
 
0.2%
171738138
58.2%
1323711
 
0.8%
1260096
 
2.0%
51513
 
0.1%
354
 
< 0.1%
2788699
26.4%

click_country
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.357656046
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-08-28T14:17:14.164863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.725860976
Coefficient of variation (CV)1.271206342
Kurtosis21.55275991
Mean1.357656046
Median Absolute Deviation (MAD)0
Skewness4.802252338
Sum4056922
Variance2.978596109
MonotonicityNot monotonic
2022-08-28T14:17:14.379829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
12852406
95.5%
1061377
 
2.1%
1129999
 
1.0%
89556
 
0.3%
67256
 
0.2%
96746
 
0.2%
26101
 
0.2%
34540
 
0.2%
53498
 
0.1%
43389
 
0.1%
ValueCountFrequency (%)
12852406
95.5%
26101
 
0.2%
34540
 
0.2%
43389
 
0.1%
53498
 
0.1%
67256
 
0.2%
73313
 
0.1%
89556
 
0.3%
96746
 
0.2%
1061377
 
2.1%
ValueCountFrequency (%)
1129999
1.0%
1061377
2.1%
96746
 
0.2%
89556
 
0.3%
73313
 
0.1%
67256
 
0.2%
53498
 
0.1%
43389
 
0.1%
34540
 
0.2%
26101
 
0.2%

click_region
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.31331435
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-08-28T14:17:14.597834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q113
median21
Q325
95-th percentile27
Maximum28
Range27
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.064006436
Coefficient of variation (CV)0.3857306383
Kurtosis-0.9755078164
Mean18.31331435
Median Absolute Deviation (MAD)4
Skewness-0.545880017
Sum54723498
Variance49.90018693
MonotonicityNot monotonic
2022-08-28T14:17:14.835830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
25804985
26.9%
21464230
15.5%
13320957
 
10.7%
8179339
 
6.0%
16164884
 
5.5%
28135793
 
4.5%
24130537
 
4.4%
20120884
 
4.0%
596979
 
3.2%
984693
 
2.8%
Other values (18)484900
16.2%
ValueCountFrequency (%)
17110
 
0.2%
216728
 
0.6%
33997
 
0.1%
430265
 
1.0%
596979
3.2%
657254
 
1.9%
764062
 
2.1%
8179339
6.0%
984693
2.8%
1021995
 
0.7%
ValueCountFrequency (%)
28135793
 
4.5%
2718711
 
0.6%
2618893
 
0.6%
25804985
26.9%
24130537
 
4.4%
2343
 
< 0.1%
2213101
 
0.4%
21464230
15.5%
20120884
 
4.0%
1934092
 
1.1%

click_referrer_type
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.838981307
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-08-28T14:17:15.207829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.15635571
Coefficient of variation (CV)0.628802319
Kurtosis9.117533472
Mean1.838981307
Median Absolute Deviation (MAD)0
Skewness2.83996653
Sum5495209
Variance1.337158529
MonotonicityNot monotonic
2022-08-28T14:17:15.421829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21602601
53.6%
11194321
40.0%
580766
 
2.7%
769798
 
2.3%
620455
 
0.7%
419820
 
0.7%
3420
 
< 0.1%
ValueCountFrequency (%)
11194321
40.0%
21602601
53.6%
3420
 
< 0.1%
419820
 
0.7%
580766
 
2.7%
620455
 
0.7%
769798
 
2.3%
ValueCountFrequency (%)
769798
 
2.3%
620455
 
0.7%
580766
 
2.7%
419820
 
0.7%
3420
 
< 0.1%
21602601
53.6%
11194321
40.0%

Interactions

2022-08-28T14:16:49.565837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:03.353829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:13.757859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:24.438830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:35.147836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:45.615828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:56.762831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:07.278830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:17.654827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:28.485828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:38.769830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:50.544828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:04.345836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:14.745827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:25.414828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:36.098831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:46.636829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:57.872828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:08.230832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:18.632832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:29.415830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:39.736836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:51.514828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:05.278835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:15.705830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:26.343832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:37.036832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:47.669830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:58.813829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:09.170830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:19.602870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:30.351833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:40.707832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:52.494831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:06.214836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:16.679836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:27.286865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:37.964827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:48.698827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:59.757833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:10.109833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:20.735830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:31.287868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:41.691834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:53.476871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:07.166832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:17.704830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:28.247829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:38.929828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:49.699828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:00.707830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:11.064828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:21.698832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:32.236830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:42.657830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:54.450830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:08.108829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:18.668831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:29.181832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:39.874829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:50.735830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:01.642834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:12.000837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:22.664828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:33.169833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:43.618828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:55.426831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:09.052829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:19.642867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:30.129830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:40.817869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:51.756830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:02.577831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:12.929830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:23.622834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:34.096830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:44.752831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:56.404829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:09.997828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:20.618830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:31.082831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:41.771830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:52.773828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:03.527828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:13.875829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:24.595835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:35.035830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:45.715837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:57.374829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:10.931833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:21.576828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:32.015836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:42.714831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:53.791835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:04.460831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:14.809831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:25.560832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:35.949835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:46.680859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:58.342832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:11.858830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:22.533834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:32.952832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:43.659828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:54.803835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:05.404829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:15.743828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:26.524828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:36.873828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:47.628870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:59.289831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:12.791847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:23.495828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:34.186830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:44.604830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:15:55.819830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:06.352832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:16.684829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:27.544831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:37.806830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-28T14:16:48.601830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-08-28T14:17:15.659864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-28T14:17:15.939859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-28T14:17:16.211829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-28T14:17:16.484863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-28T14:17:00.443864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-28T14:17:03.223864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0user_idsession_idsession_startsession_sizeclick_article_idclick_timestampclick_environmentclick_deviceGroupclick_osclick_countryclick_regionclick_referrer_type
00015068254232717372017-10-01 02:37:0321575412017-10-01 03:00:28.02043201202
11015068254232717372017-10-01 02:37:032688662017-10-01 03:00:58.02043201202
22115068254262677382017-10-01 02:37:0622358402017-10-01 03:03:37.95141171162
33115068254262677382017-10-01 02:37:062966632017-10-01 03:04:07.95141171162
44215068254352997392017-10-01 02:37:1521195922017-10-01 03:04:50.57541171242
55215068254352997392017-10-01 02:37:152309702017-10-01 03:05:20.57541171242
66315068254427047402017-10-01 02:37:2222360652017-10-01 03:12:16.9424321211
77315068254427047402017-10-01 02:37:2222362942017-10-01 03:12:46.9424321211
88415068255281357412017-10-01 02:38:482489152017-10-01 03:02:07.59341171171
99415068255281357412017-10-01 02:38:482444882017-10-01 03:02:37.59341171171

Last rows

Unnamed: 0user_idsession_idsession_startsession_sizeclick_article_idclick_timestampclick_environmentclick_deviceGroupclick_osclick_countryclick_regionclick_referrer_type
298817129881713497915082113691043272017-10-17 03:36:0972117322017-10-17 03:40:36.3004321251
298817229881723497915082113691043272017-10-17 03:36:097163462017-10-17 03:43:17.1874321251
298817329881733497915082113691043272017-10-17 03:36:0973311492017-10-17 03:45:44.1164321251
298817429881743497915082113691043272017-10-17 03:36:0971574782017-10-17 03:46:14.1164321251
298817529881751005115082113721583282017-10-17 03:36:1222114422017-10-17 03:38:47.3024321251
298817629881761005115082113721583282017-10-17 03:36:122849112017-10-17 03:39:17.3024321251
2988177298817732289615082113763023292017-10-17 03:36:162307602017-10-17 03:41:12.52041171252
2988178298817832289615082113763023292017-10-17 03:36:1621575072017-10-17 03:41:42.52041171252
2988179298817912371815082113791893302017-10-17 03:36:1922344812017-10-17 03:38:33.5834321252
2988180298818012371815082113791893302017-10-17 03:36:1922335782017-10-17 03:39:03.5834321252